OpenRaiser/Intern-Atlas huggingface dataset:
Here's My Point: Joint Pointer Architecture for Argument Mining
Venue: arXiv cs.CL 2017
Year: 2017

One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text. In order to determine this structure, one must understand how different individual components of the overall argument are linked. General consensus in this field dictates that the argument components form a hierarchy of persuasion, which manifests itself in a tree structure. This work provides the first neural network-based approach to argumentation mining, focusing on the two tasks of extracting links between argument components, and classifying types of argument components. In order to solve this problem, we propose to use a joint model that is based on a Pointer Network architecture. A Pointer Network is appealing for this task for the following reasons: 1) It takes into account the sequential nature of argument components; 2) By construction, it enforces certain properties of the tree structure present in argument relations; 3) The hidden representations can be applied to auxiliary tasks. In order to extend the contribution of the original Pointer Network model, we construct a joint model that simultaneously attempts to learn the type of argument component, as well as continuing to predict links between argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, achieving far superior performance than a regular Pointer Network model. Our results show that optimizing for both tasks, and adding a fully-connected layer prior to recurrent neural network input, is crucial for high performance.
Efficient isogeometric thin shell formulations for soft biological materials
Venue: arXiv cs.CE 2017
Year: 2017

This paper presents three different constitutive approaches to model thin rotation-free shells based on the Kirchhoff-Love hypothesis. One approach is based on numerical integration through the shell thickness while the other two approaches do not need any numerical integration and so they are computationally more efficient. The formulation is designed for large deformations and allows for geometrical and material nonlinearities, which makes it very suitable for the modeling of soft tissues. Furthermore, six different isotropic and anisotropic material models, which are commonly used to model soft biological materials, are examined for the three proposed constitutive approaches. Following an isogeometric approach, NURBS-based finite elements are used for the discretization of the shell surface. Several numerical examples are investigated to demonstrate the capabilities of the formulation. Those include the contact simulation during balloon angioplasty.
Detecting Plagiarism based on the Creation Process
Venue: arXiv cs.OH 2017
Year: 2017

All methodologies for detecting plagiarism to date have focused on the final digital "outcome", such as a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by the macro recorders found in most office applications. We look at an author's interaction logs with the software used to create the work. Detection relies on comparing the histograms of multiple logs' command use. A work is classified as plagiarism if its log deviates too much from logs of "honestly created" works or if its log is too similar to another log. The technique supports the detection of plagiarism for digital outcomes that stem from \emph{unique} tasks, such as theses and \emph{equal} tasks such as assignments for which the same problem sets are solved by multiple students. Focusing on the latter case, we evaluate this approach using logs collected by an interactive development environment (IDE) from more than sixty students who completed three programming assignments.
Generalized Hamming weights of three classes of linear codes
Venue: arXiv cs.IT 2017
Year: 2017

The generalized Hamming weights of a linear code have been extensively studied since Wei first use them to characterize the cryptography performance of a linear code over the wire-tap channel of type II. In this paper, we investigate the generalized Hamming weights of three classes of linear codes constructed through defining sets and determine them partly for some cases. Particularly, in the semiprimitive case we solve an problem left in Yang et al. (IEEE Trans. Inf. Theory 61(9): 4905--4913, 2015).
Ex-post Stable and Fair Payoff Allocation for Renewable Energy Aggregation
Venue: arXiv cs.GT 2017
Year: 2017

Aggregating statistically diverse renewable power producers (RPPs) is an effective way to reduce the uncertainty of the RPPs. The key question in aggregation of RPPs is how to allocate payoffs among the RPPs. In this paper, a payoff allocation mechanism (PAM) with a simple closed-form expression is proposed: It achieves stability (in the core) and fairness both in the "ex-post" sense, i.e., for all possible realizations of renewable power generation. Furthermore, this PAM can in fact be derived from the competitive equilibrium in a market. The proposed PAM is evaluated in a simulation study with ten wind power producers in the PJM interconnection.
POMDP Structural Results for Controlled Sensing
Venue: arXiv cs.SY 2017
Year: 2017

This article provides a short review of some structural results in controlled sensing when the problem is formulated as a partially observed Markov decision process. In particular, monotone value functions, Blackwell dominance and quickest detection are described.
Interactive Movie Recommendation Through Latent Semantic Analysis and Storytelling
Venue: arXiv cs.IR 2017
Year: 2017

Recommendation has become one of the most important components of online services for improving sale records, however visualization work for online recommendation is still very limited. This paper presents an interactive recommendation approach with the following two components. First, rating records are the most widely used data for online recommendation, but they are often processed in high-dimensional spaces that can not be easily understood or interacted with. We propose a Latent Semantic Model (LSM) that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. Our approach emphasizes interactivity, explicit user input, and semantic information convey; thus it can be used by general users without any knowledge of recommendation or visualization algorithms. We validate our model with data statistics and demonstrate our approach with case studies from the MovieLens100K dataset. Our approaches of latent semantic analysis and interactive recommendation can also be extended to other network-based visualization applications, including various online recommendation systems.
Outlier Robust Online Learning
Venue: arXiv cs.LG 2017
Year: 2017

We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approach. ORL is simple to implement and has provable robustness guarantee -- in stark contrast to existing online learning approaches that are generally fragile to outliers. We specialize the ORL approach for two concrete cases: online robust principal component analysis and online linear regression. We demonstrate the efficiency and robustness advantages of ORL through comprehensive simulations and predicting image tags on a large-scale data set. We also discuss extension of the ORL to distributed learning and provide experimental evaluations.
Raising Graphs From Randomness to Reveal Information Networks
Venue: arXiv cs.SI 2017
Year: 2017

We analyze the fine-grained connections between the average degree and the power-law degree distribution exponent in growing information networks. Our starting observation is a power-law degree distribution with a decreasing exponent and increasing average degree as a function of the network size. Our experiments are based on three Twitter at-mention networks and three more from the Koblenz Network Collection. We observe that popular network models cannot explain decreasing power-law degree distribution exponent and increasing average degree at the same time. We propose a model that is the combination of exponential growth, and a power-law developing network, in which new "homophily" edges are continuously added to nodes proportional to their current homophily degree. Parameters of the average degree growth and the power-law degree distribution exponent functions depend on the ratio of the network growth exponent parameters. Specifically, we connect the growth of the average degree to the decreasing exponent of the power-law degree distribution. Prior to our work, only one of the two cases were handled. Existing models and even their combinations can only reproduce some of our key new observations in growing information networks.
Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation
Venue: arXiv cs.AI 2017
Year: 2017

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [Laird (2012)]) adopt a classical symbolic approach, some (e.g. LEABRA [O'Reilly and Munakata (2000)]) are based on a purely connectionist model, while others (e.g. CLARION [Sun (2006)] adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available [Kurup and Chandrasekaran (2007)]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter Gärdenfors [Gärdenfors (2000)] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gärdenfors [Gärdenfors (1997)] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one.
Stance detection in online discussions
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes our system created to detect stance in online discussions. The goal is to identify whether the author of a comment is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific features. The system was originally developed to detect stance in English tweets. We adapted it to process Czech news commentaries.
End-to-End Attention based Text-Dependent Speaker Verification
Venue: arXiv cs.CL 2017
Year: 2017

A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process --- directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows $10$ "Hey Cortana" speaker verification task.
Robust method for finding sparse solutions to linear inverse problems using an L2 regularization
Venue: arXiv cs.NA 2017
Year: 2017

We analyzed the performance of a biologically inspired algorithm called the Corrected Projections Algorithm (CPA) when a sparseness constraint is required to unambiguously reconstruct an observed signal using atoms from an overcomplete dictionary. By changing the geometry of the estimation problem, CPA gives an analytical expression for a binary variable that indicates the presence or absence of a dictionary atom using an L2 regularizer. The regularized solution can be implemented using an efficient real-time Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it very robust to noise, and CPA outperforms other methods in identifying known atoms in the presence of strong novel atoms in the signal.
Knowledge Engineering for Hybrid Deductive Databases
Venue: arXiv cs.DB 2017
Year: 2017

Modern knowledge base systems frequently need to combine a collection of databases in different formats: e.g., relational databases, XML databases, rule bases, ontologies, etc. In the deductive database system DDBASE, we can manage these different formats of knowledge and reason about them. Even the file systems on different computers can be part of the knowledge base. Often, it is necessary to handle different versions of a knowledge base. E.g., we might want to find out common parts or differences of two versions of a relational database. We will examine the use of abstractions of rule bases by predicate dependency and rule predicate graphs. Also the proof trees of derived atoms can help to compare different versions of a rule base. Moreover, it might be possible to have derivations joining rules with other formalisms of knowledge representation. Ontologies have shown their benefits in many applications of intelligent systems, and there have been many proposals for rule languages compatible with the semantic web stack, e.g., SWRL, the semantic web rule language. Recently, ontologies are used in hybrid systems for specifying the provenance of the different components.
A Framework for Extending microKanren with Constraints
Venue: arXiv cs.PL 2017
Year: 2017

We present a framework for building CLP languages with symbolic constraints based on microKanren, a domain-specific logic language shallowly embedded in Racket. We rely on Racket's macro system to generate a constraint solver and other components of the microKanren embedding. The framework itself and the constraints' implementations amounts to just over 100 lines of code. Our framework is both a teachable implementation for CLP as well as a test-bed and prototyping tool for symbolic constraint systems.
Confluence of Conditional Term Rewrite Systems via Transformations
Venue: arXiv cs.LO 2017
Year: 2017

Conditional term rewriting is an intuitive yet complex extension of term rewriting. In order to benefit from the simpler framework of unconditional rewriting, transformations have been defined to eliminate the conditions of conditional term rewrite systems. Recent results provide confluence criteria for conditional term rewrite systems via transformations, yet they are restricted to CTRSs with certain syntactic properties like weak left-linearity. These syntactic properties imply that the transformations are sound for the given CTRS. This paper shows how to use transformations to prove confluence of operationally terminating, right-stable deterministic conditional term rewrite systems without the necessity of soundness restrictions. For this purpose, it is shown that certain rewrite strategies, in particular almost U-eagerness and innermost rewriting, always imply soundness.
An Environment for Analyzing Space Optimizations in Call-by-Need Functional Languages
Venue: arXiv cs.PL 2017
Year: 2017

We present an implementation of an interpreter LRPi for the call-by-need calculus LRP, based on a variant of Sestoft's abstract machine Mark 1, extended with an eager garbage collector. It is used as a tool for exact space usage analyses as a support for our investigations into space improvements of call-by-need calculi.
On the Performance of Zero-Forcing Processing in Multi-Way Massive MIMO Relay Networks
Venue: arXiv cs.IT 2017
Year: 2017

We consider a multi-way massive multiple-input multiple-output relay network with zero-forcing processing at the relay. By taking into account the time-division duplex protocol with channel estimation, we derive an analytical approximation of the spectral efficiency. This approximation is very tight and simple which enables us to analyze the system performance, as well as, to compare the spectral efficiency with zero-forcing and maximum-ratio processing. Our results show that by using a very large number of relay antennas and with the zero-forcing technique, we can simultaneously serve many active users in the same time-frequency resource, each with high spectral efficiency.
Picturing Indefinite Causal Structure
Venue: arXiv cs.OH 2017
Year: 2017

Following on from the notion of (first-order) causality, which generalises the notion of being tracepreserving from CP-maps to abstract processes, we give a characterization for the most general kind of map which sends causal processes to causal processes. These new, second-order causal processes enable us to treat the input processes as 'local laboratories' whose causal ordering needs not be fixed in advance. Using this characterization, we give a fully-diagrammatic proof of a non-trivial theorem: namely that being causality-preserving on separable processes implies being 'completely' causality preserving. That is, causality is preserved even when the 'local laboratories' are allowed to have ancilla systems. An immediate consequence is that preserving causality is separable processes is equivalence to preserving causality for strongly non-signalling (a.k.a. localizable) processes.
Fuzzy finite element model updating using metaheuristic optimization algorithms
Venue: arXiv cs.AI 2017
Year: 2017

In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreducible, and may decrease the accuracy of the finite element model updating process. However, uncertainty quantification methods can be used to identify the uncertainty in the updating parameters. In this paper, the uncertainties associated with the modal parameters are defined as fuzzy membership functions, while the model updating procedure is defined as an optimization problem at each α-cut level. To determine the membership functions of the updated parameters, an objective function is defined and minimized using two metaheuristic optimization algorithms: ant colony optimization (ACO) and particle swarm optimization (PSO). A structural example is used to investigate the accuracy of the fuzzy model updating strategy using the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy finite element model updating are compared with the Bayesian model updating results.
A Taxonomy of Privacy Constructs for Privacy-Sensitive Robotics
Venue: arXiv cs.CY 2017
Year: 2017

The introduction of robots into our society will also introduce new concerns about personal privacy. In order to study these concerns, we must do human-subject experiments that involve measuring privacy-relevant constructs. This paper presents a taxonomy of privacy constructs based on a review of the privacy literature. Future work in operationalizing privacy constructs for HRI studies is also discussed.
Effect of selfish choices in deferred acceptance with short lists
Venue: arXiv cs.GT 2017
Year: 2017

We study the outcome of deferred acceptance when prospective medical residents can only apply to a limited set of hospitals. This limitation requires residents to make a strategic choice about the quality of hospitals they apply to. Through a mix of theoretical and experimental results, we study the effect of this strategic choice on the preferences submitted by participants, as well as on the overall welfare. We find that residents' choices in our model mimic the behavior observed in real systems where individuals apply to a mix of positions consisting mostly of places where they are reasonably likely to get accepted, as well as a few "reach" applications to hospitals of very high quality, and a few "safe" applications to hospitals of lower than their expected level. Surprisingly, the number of such "safe" applications is not monotone in the number of allowed applications. We also find that selfish behavior can hurt social welfare, but the deterioration of overall welfare is very minimal.
Design, Control and Visual Navigation of the DelftaCopter
Venue: arXiv cs.RO 2017
Year: 2017

To participate in the Outback Medical Express UAV Challenge 2016, a vehicle was designed and tested that can hover precisely, take-off and land vertically, fly fast forward efficiently and use computer vision to locate a person and a suitable landing location. A rotor blade was designed that can deliver sufficient thrust in hover, while still being efficient in fast forward flight. Energy measurements and windtunnel tests were performed. A rotor-head and corresponding control algorithms were developed to allow transitioning flight with the non-conventional rotor dynamics. Dedicated electronics were designed that meet vehicle needs and regulations to allow safe flight beyond visual line of sight. Vision based search and guidance algorithms were developed and tested. Flight tests and a competition participation illustrate the applicability of the DelftaCopter concept.
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms
Venue: arXiv cs.AI 2017
Year: 2017

The high variance issue in unbiased policy-gradient methods such as VPG and REINFORCE is typically mitigated by adding a baseline. However, the baseline fitting itself suffers from the underfitting or the overfitting problem. In this paper, we develop a K-fold method for baseline estimation in policy gradient algorithms. The parameter K is the baseline estimation hyperparameter that can adjust the bias-variance trade-off in the baseline estimates. We demonstrate the usefulness of our approach via two state-of-the-art policy gradient algorithms on three MuJoCo locomotive control tasks.